Phase 1 – Python & Data Skills
- Python for data: syntax, modules, virtual environments
- NumPy, Pandas, visualization
- Git basics for collaborative projects
Active IT company · Mansarovar, Jaipur
Move from Python fundamentals to practical ML: data preparation, model training, evaluation, and shipping simple AI features — mentored inside a live software company environment in Jaipur.
This AI and machine learning course in Jaipur balances fundamentals with portfolio projects so you can speak confidently in technical interviews.
You’ll work on prediction and classification problems with realistic constraints — data cleaning, leakage checks, and clear evaluation — then present outcomes to mentors.
End‑to‑end tabular ML project with clean reporting
Computer vision or NLP mini‑project (track‑dependent)
Capstone aligned to industry‑style milestones
Portfolio documentation and demo notebooks are reviewed before you ship to interviews.
Graduate with portfolio projects, internship exposure for eligible learners, and clearer positioning for junior ML and data-centric roles.
Internship opportunities for eligible students
Resume and GitHub / portfolio review
Mock interviews (technical + HR)
Guidance for startups, agencies, and product teams
Request detailed fee structure — we’ll share plans and any applicable scholarships on call.
Enroll now Prefer WhatsApp? Message usWe email the syllabus outline and fee options for the next batch. Typical reply within one business day.
WhatsApp: +91 9717047610
No spam—only batch and course updates relevant to your request.
Course-specific FAQs for our AI & Machine Learning training in Jaipur.
Basic programming helps, but we include a structured Python ramp-up for beginners.
Yes — live online batches mirror the same milestones and mentor reviews.
Yes, upon meeting completion criteria including projects and attendance.
8–16 GB RAM recommended; GPU is helpful but not mandatory for the core curriculum.
Expect 12–18 hours including self-practice and assignments.
We cover the essentials you need for practical ML; heavy theory is kept minimal and applied.